The binary cluster model (BCM) and the two-frequency shell model (TFSM) have been used to study the ground state matter densities of neutron-rich ⁶He and ¹¹Li halo nuclei. Calculations show that both models provide a good description on the matter density distribution of above nuclei. The root-mean square (rms) proton, neutron and matter radii of these halo nuclei obtained by TFSM have been successfully obtained. The elastic charge form factors for these halo nuclei are studied through combining the charge density distribution obtained by TFSM with the plane wave Born approximation (PWBA).

Successfully, theoretical equations were established to study the effect of solvent polarities on the electron current density, fill factor and efficiencies of Tris (8-hydroxy) quinoline aluminum (Alq3)/ ZnO solar cells. Three different solvents studied in this theoretical works, namely 1-propanol, ethanol and acetonitrile. The quantum model of transition energy in donor–acceptor system was used to derive a current formula. After that, it has been used to calculate the fill factor and the efficiency of the solar cell. The calculations indicated that the efficiency of the solar cell is influenced by the polarity of solvents. The best performance was for the solar cell based on acetonitrile as a solvent with electron current density of (5.0

Stabilization of phenol trapped by agricultural waste: a study of the influence of ambient temperature on the adsorbed phenol

This paper aims to make a historical review of jet grouting techniques and encountered problems at different sites in several countries.  This review is a good guide to understanding the performance and limitations of improved soils or lands. The basic concept of jet grouting technology is to use cement as a binder to accelerate the hardening process of an admixture of material grout and soil. The different case history was conducted in both sand soil and clay soil in the horizontal and vertical direction. Other papers on field construction showed that the grout can be gelled within 5-10 minutes. Due to different cases and studies, these will help improve soil by supporting the foundation load with a minimal settlement.

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

      A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs lengths and their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy. The optimization carried out is subjected to constraints that ensure a safe structure aga

Abstract

The fiber Bragg grating (FBG) technology has been rapidly applied in the sensing technology field. In this work, uniform FBG was used as pressure sensor based on measuring related Bragg wavelength shift. The pressure was applied directly by air compressor to the sensor and the pressure was ranged from 1 to 6 bar.

      This sensor also was affected by the external temperature so as a result it could be used as a temperature sensor. This sensor could be used to monitor the pressure of dams. It has been shown from the result that the sensor is very sensitive to the pressure and the sensitivity was (67 pm\bar) and is very sensitive to temperature and the sensitivity was (10p

This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-Watson). The estimates can be obtained by solve nonparametric quantile regression problem which means minimizing the quantile regression objective functions and using the approach of varying coefficient models. The main goal is discussing the comparison between the estimators of the two nonparametric methods and adopting the best one between them

Geodesy is concerned with the relative positioning of points and the gravity field of the earth. For this task, a well-defined coordinate system is needed on which measurements are normally tied to a set of reference points called a geodetic datum (geoid or ellipsoid). The Global Positioning System GPS gives accurately the three-dimensional position of a point (latitude, longitude, and ellipsoidal height) and can measure under all weather conditions. The coordinates of the GPS reference to the World Geodetic System1984 (WGS 84), a global ellipsoid having its origin as the mass center of the earth, and height, referenced to the surface of the ellipsoid . In this research , using RTK-DGPS technique Data collection for study local and level

In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi